# -*- coding: utf-8 -*- # Copyright 2024 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import dataclasses import json # type: ignore import logging from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union import warnings from google.api_core import gapic_v1, operations_v1, rest_helpers, rest_streaming from google.api_core import exceptions as core_exceptions from google.api_core import retry as retries from google.auth import credentials as ga_credentials # type: ignore from google.auth.transport.requests import AuthorizedSession # type: ignore from google.longrunning import operations_pb2 # type: ignore from google.protobuf import empty_pb2 # type: ignore from google.protobuf import json_format from requests import __version__ as requests_version from google.ai.generativelanguage_v1alpha.types import tuned_model as gag_tuned_model from google.ai.generativelanguage_v1alpha.types import model, model_service from google.ai.generativelanguage_v1alpha.types import tuned_model from .base import DEFAULT_CLIENT_INFO as BASE_DEFAULT_CLIENT_INFO from .rest_base import _BaseModelServiceRestTransport try: OptionalRetry = Union[retries.Retry, gapic_v1.method._MethodDefault, None] except AttributeError: # pragma: NO COVER OptionalRetry = Union[retries.Retry, object, None] # type: ignore try: from google.api_core import client_logging # type: ignore CLIENT_LOGGING_SUPPORTED = True # pragma: NO COVER except ImportError: # pragma: NO COVER CLIENT_LOGGING_SUPPORTED = False _LOGGER = logging.getLogger(__name__) DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo( gapic_version=BASE_DEFAULT_CLIENT_INFO.gapic_version, grpc_version=None, rest_version=f"requests@{requests_version}", ) class ModelServiceRestInterceptor: """Interceptor for ModelService. Interceptors are used to manipulate requests, request metadata, and responses in arbitrary ways. Example use cases include: * Logging * Verifying requests according to service or custom semantics * Stripping extraneous information from responses These use cases and more can be enabled by injecting an instance of a custom subclass when constructing the ModelServiceRestTransport. .. code-block:: python class MyCustomModelServiceInterceptor(ModelServiceRestInterceptor): def pre_create_tuned_model(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_create_tuned_model(self, response): logging.log(f"Received response: {response}") return response def pre_delete_tuned_model(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def pre_get_model(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_get_model(self, response): logging.log(f"Received response: {response}") return response def pre_get_tuned_model(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_get_tuned_model(self, response): logging.log(f"Received response: {response}") return response def pre_list_models(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_list_models(self, response): logging.log(f"Received response: {response}") return response def pre_list_tuned_models(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_list_tuned_models(self, response): logging.log(f"Received response: {response}") return response def pre_update_tuned_model(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_update_tuned_model(self, response): logging.log(f"Received response: {response}") return response transport = ModelServiceRestTransport(interceptor=MyCustomModelServiceInterceptor()) client = ModelServiceClient(transport=transport) """ def pre_create_tuned_model( self, request: model_service.CreateTunedModelRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]], ) -> Tuple[ model_service.CreateTunedModelRequest, Sequence[Tuple[str, Union[str, bytes]]] ]: """Pre-rpc interceptor for create_tuned_model Override in a subclass to manipulate the request or metadata before they are sent to the ModelService server. """ return request, metadata def post_create_tuned_model( self, response: operations_pb2.Operation ) -> operations_pb2.Operation: """Post-rpc interceptor for create_tuned_model Override in a subclass to manipulate the response after it is returned by the ModelService server but before it is returned to user code. """ return response def pre_delete_tuned_model( self, request: model_service.DeleteTunedModelRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]], ) -> Tuple[ model_service.DeleteTunedModelRequest, Sequence[Tuple[str, Union[str, bytes]]] ]: """Pre-rpc interceptor for delete_tuned_model Override in a subclass to manipulate the request or metadata before they are sent to the ModelService server. """ return request, metadata def pre_get_model( self, request: model_service.GetModelRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]], ) -> Tuple[model_service.GetModelRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for get_model Override in a subclass to manipulate the request or metadata before they are sent to the ModelService server. """ return request, metadata def post_get_model(self, response: model.Model) -> model.Model: """Post-rpc interceptor for get_model Override in a subclass to manipulate the response after it is returned by the ModelService server but before it is returned to user code. """ return response def pre_get_tuned_model( self, request: model_service.GetTunedModelRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]], ) -> Tuple[ model_service.GetTunedModelRequest, Sequence[Tuple[str, Union[str, bytes]]] ]: """Pre-rpc interceptor for get_tuned_model Override in a subclass to manipulate the request or metadata before they are sent to the ModelService server. """ return request, metadata def post_get_tuned_model( self, response: tuned_model.TunedModel ) -> tuned_model.TunedModel: """Post-rpc interceptor for get_tuned_model Override in a subclass to manipulate the response after it is returned by the ModelService server but before it is returned to user code. """ return response def pre_list_models( self, request: model_service.ListModelsRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]], ) -> Tuple[ model_service.ListModelsRequest, Sequence[Tuple[str, Union[str, bytes]]] ]: """Pre-rpc interceptor for list_models Override in a subclass to manipulate the request or metadata before they are sent to the ModelService server. """ return request, metadata def post_list_models( self, response: model_service.ListModelsResponse ) -> model_service.ListModelsResponse: """Post-rpc interceptor for list_models Override in a subclass to manipulate the response after it is returned by the ModelService server but before it is returned to user code. """ return response def pre_list_tuned_models( self, request: model_service.ListTunedModelsRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]], ) -> Tuple[ model_service.ListTunedModelsRequest, Sequence[Tuple[str, Union[str, bytes]]] ]: """Pre-rpc interceptor for list_tuned_models Override in a subclass to manipulate the request or metadata before they are sent to the ModelService server. """ return request, metadata def post_list_tuned_models( self, response: model_service.ListTunedModelsResponse ) -> model_service.ListTunedModelsResponse: """Post-rpc interceptor for list_tuned_models Override in a subclass to manipulate the response after it is returned by the ModelService server but before it is returned to user code. """ return response def pre_update_tuned_model( self, request: model_service.UpdateTunedModelRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]], ) -> Tuple[ model_service.UpdateTunedModelRequest, Sequence[Tuple[str, Union[str, bytes]]] ]: """Pre-rpc interceptor for update_tuned_model Override in a subclass to manipulate the request or metadata before they are sent to the ModelService server. """ return request, metadata def post_update_tuned_model( self, response: gag_tuned_model.TunedModel ) -> gag_tuned_model.TunedModel: """Post-rpc interceptor for update_tuned_model Override in a subclass to manipulate the response after it is returned by the ModelService server but before it is returned to user code. """ return response def pre_get_operation( self, request: operations_pb2.GetOperationRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]], ) -> Tuple[ operations_pb2.GetOperationRequest, Sequence[Tuple[str, Union[str, bytes]]] ]: """Pre-rpc interceptor for get_operation Override in a subclass to manipulate the request or metadata before they are sent to the ModelService server. """ return request, metadata def post_get_operation( self, response: operations_pb2.Operation ) -> operations_pb2.Operation: """Post-rpc interceptor for get_operation Override in a subclass to manipulate the response after it is returned by the ModelService server but before it is returned to user code. """ return response def pre_list_operations( self, request: operations_pb2.ListOperationsRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]], ) -> Tuple[ operations_pb2.ListOperationsRequest, Sequence[Tuple[str, Union[str, bytes]]] ]: """Pre-rpc interceptor for list_operations Override in a subclass to manipulate the request or metadata before they are sent to the ModelService server. """ return request, metadata def post_list_operations( self, response: operations_pb2.ListOperationsResponse ) -> operations_pb2.ListOperationsResponse: """Post-rpc interceptor for list_operations Override in a subclass to manipulate the response after it is returned by the ModelService server but before it is returned to user code. """ return response @dataclasses.dataclass class ModelServiceRestStub: _session: AuthorizedSession _host: str _interceptor: ModelServiceRestInterceptor class ModelServiceRestTransport(_BaseModelServiceRestTransport): """REST backend synchronous transport for ModelService. Provides methods for getting metadata information about Generative Models. This class defines the same methods as the primary client, so the primary client can load the underlying transport implementation and call it. It sends JSON representations of protocol buffers over HTTP/1.1 """ def __init__( self, *, host: str = "generativelanguage.googleapis.com", credentials: Optional[ga_credentials.Credentials] = None, credentials_file: Optional[str] = None, scopes: Optional[Sequence[str]] = None, client_cert_source_for_mtls: Optional[Callable[[], Tuple[bytes, bytes]]] = None, quota_project_id: Optional[str] = None, client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO, always_use_jwt_access: Optional[bool] = False, url_scheme: str = "https", interceptor: Optional[ModelServiceRestInterceptor] = None, api_audience: Optional[str] = None, ) -> None: """Instantiate the transport. Args: host (Optional[str]): The hostname to connect to (default: 'generativelanguage.googleapis.com'). credentials (Optional[google.auth.credentials.Credentials]): The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. credentials_file (Optional[str]): A file with credentials that can be loaded with :func:`google.auth.load_credentials_from_file`. This argument is ignored if ``channel`` is provided. scopes (Optional(Sequence[str])): A list of scopes. This argument is ignored if ``channel`` is provided. client_cert_source_for_mtls (Callable[[], Tuple[bytes, bytes]]): Client certificate to configure mutual TLS HTTP channel. It is ignored if ``channel`` is provided. quota_project_id (Optional[str]): An optional project to use for billing and quota. client_info (google.api_core.gapic_v1.client_info.ClientInfo): The client info used to send a user-agent string along with API requests. If ``None``, then default info will be used. Generally, you only need to set this if you are developing your own client library. always_use_jwt_access (Optional[bool]): Whether self signed JWT should be used for service account credentials. url_scheme: the protocol scheme for the API endpoint. Normally "https", but for testing or local servers, "http" can be specified. """ # Run the base constructor # TODO(yon-mg): resolve other ctor params i.e. scopes, quota, etc. # TODO: When custom host (api_endpoint) is set, `scopes` must *also* be set on the # credentials object super().__init__( host=host, credentials=credentials, client_info=client_info, always_use_jwt_access=always_use_jwt_access, url_scheme=url_scheme, api_audience=api_audience, ) self._session = AuthorizedSession( self._credentials, default_host=self.DEFAULT_HOST ) self._operations_client: Optional[operations_v1.AbstractOperationsClient] = None if client_cert_source_for_mtls: self._session.configure_mtls_channel(client_cert_source_for_mtls) self._interceptor = interceptor or ModelServiceRestInterceptor() self._prep_wrapped_messages(client_info) @property def operations_client(self) -> operations_v1.AbstractOperationsClient: """Create the client designed to process long-running operations. This property caches on the instance; repeated calls return the same client. """ # Only create a new client if we do not already have one. if self._operations_client is None: http_options: Dict[str, List[Dict[str, str]]] = { "google.longrunning.Operations.GetOperation": [ { "method": "get", "uri": "/v1alpha/{name=tunedModels/*/operations/*}", }, { "method": "get", "uri": "/v1alpha/{name=generatedFiles/*/operations/*}", }, { "method": "get", "uri": "/v1alpha/{name=models/*/operations/*}", }, ], "google.longrunning.Operations.ListOperations": [ { "method": "get", "uri": "/v1alpha/{name=tunedModels/*}/operations", }, { "method": "get", "uri": "/v1alpha/{name=models/*}/operations", }, ], } rest_transport = operations_v1.OperationsRestTransport( host=self._host, # use the credentials which are saved credentials=self._credentials, scopes=self._scopes, http_options=http_options, path_prefix="v1alpha", ) self._operations_client = operations_v1.AbstractOperationsClient( transport=rest_transport ) # Return the client from cache. return self._operations_client class _CreateTunedModel( _BaseModelServiceRestTransport._BaseCreateTunedModel, ModelServiceRestStub ): def __hash__(self): return hash("ModelServiceRestTransport.CreateTunedModel") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None, ): uri = transcoded_request["uri"] method = transcoded_request["method"] headers = dict(metadata) headers["Content-Type"] = "application/json" response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, ) return response def __call__( self, request: model_service.CreateTunedModelRequest, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> operations_pb2.Operation: r"""Call the create tuned model method over HTTP. Args: request (~.model_service.CreateTunedModelRequest): The request object. Request to create a TunedModel. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: ~.operations_pb2.Operation: This resource represents a long-running operation that is the result of a network API call. """ http_options = ( _BaseModelServiceRestTransport._BaseCreateTunedModel._get_http_options() ) request, metadata = self._interceptor.pre_create_tuned_model( request, metadata ) transcoded_request = _BaseModelServiceRestTransport._BaseCreateTunedModel._get_transcoded_request( http_options, request ) body = _BaseModelServiceRestTransport._BaseCreateTunedModel._get_request_body_json( transcoded_request ) # Jsonify the query params query_params = _BaseModelServiceRestTransport._BaseCreateTunedModel._get_query_params_json( transcoded_request ) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER request_url = "{host}{uri}".format( host=self._host, uri=transcoded_request["uri"] ) method = transcoded_request["method"] try: request_payload = json_format.MessageToJson(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.ai.generativelanguage_v1alpha.ModelServiceClient.CreateTunedModel", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "CreateTunedModel", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = ModelServiceRestTransport._CreateTunedModel._get_response( self._host, metadata, query_params, self._session, timeout, transcoded_request, body, ) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = operations_pb2.Operation() json_format.Parse(response.content, resp, ignore_unknown_fields=True) resp = self._interceptor.post_create_tuned_model(resp) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER try: response_payload = json_format.MessageToJson(resp) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.ai.generativelanguage_v1alpha.ModelServiceClient.create_tuned_model", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "CreateTunedModel", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _DeleteTunedModel( _BaseModelServiceRestTransport._BaseDeleteTunedModel, ModelServiceRestStub ): def __hash__(self): return hash("ModelServiceRestTransport.DeleteTunedModel") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None, ): uri = transcoded_request["uri"] method = transcoded_request["method"] headers = dict(metadata) headers["Content-Type"] = "application/json" response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), ) return response def __call__( self, request: model_service.DeleteTunedModelRequest, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ): r"""Call the delete tuned model method over HTTP. Args: request (~.model_service.DeleteTunedModelRequest): The request object. Request to delete a TunedModel. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. """ http_options = ( _BaseModelServiceRestTransport._BaseDeleteTunedModel._get_http_options() ) request, metadata = self._interceptor.pre_delete_tuned_model( request, metadata ) transcoded_request = _BaseModelServiceRestTransport._BaseDeleteTunedModel._get_transcoded_request( http_options, request ) # Jsonify the query params query_params = _BaseModelServiceRestTransport._BaseDeleteTunedModel._get_query_params_json( transcoded_request ) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER request_url = "{host}{uri}".format( host=self._host, uri=transcoded_request["uri"] ) method = transcoded_request["method"] try: request_payload = json_format.MessageToJson(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.ai.generativelanguage_v1alpha.ModelServiceClient.DeleteTunedModel", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "DeleteTunedModel", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = ModelServiceRestTransport._DeleteTunedModel._get_response( self._host, metadata, query_params, self._session, timeout, transcoded_request, ) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) class _GetModel(_BaseModelServiceRestTransport._BaseGetModel, ModelServiceRestStub): def __hash__(self): return hash("ModelServiceRestTransport.GetModel") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None, ): uri = transcoded_request["uri"] method = transcoded_request["method"] headers = dict(metadata) headers["Content-Type"] = "application/json" response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), ) return response def __call__( self, request: model_service.GetModelRequest, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> model.Model: r"""Call the get model method over HTTP. Args: request (~.model_service.GetModelRequest): The request object. Request for getting information about a specific Model. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: ~.model.Model: Information about a Generative Language Model. """ http_options = ( _BaseModelServiceRestTransport._BaseGetModel._get_http_options() ) request, metadata = self._interceptor.pre_get_model(request, metadata) transcoded_request = ( _BaseModelServiceRestTransport._BaseGetModel._get_transcoded_request( http_options, request ) ) # Jsonify the query params query_params = ( _BaseModelServiceRestTransport._BaseGetModel._get_query_params_json( transcoded_request ) ) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER request_url = "{host}{uri}".format( host=self._host, uri=transcoded_request["uri"] ) method = transcoded_request["method"] try: request_payload = type(request).to_json(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.ai.generativelanguage_v1alpha.ModelServiceClient.GetModel", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "GetModel", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = ModelServiceRestTransport._GetModel._get_response( self._host, metadata, query_params, self._session, timeout, transcoded_request, ) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = model.Model() pb_resp = model.Model.pb(resp) json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_get_model(resp) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER try: response_payload = model.Model.to_json(response) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.ai.generativelanguage_v1alpha.ModelServiceClient.get_model", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "GetModel", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _GetTunedModel( _BaseModelServiceRestTransport._BaseGetTunedModel, ModelServiceRestStub ): def __hash__(self): return hash("ModelServiceRestTransport.GetTunedModel") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None, ): uri = transcoded_request["uri"] method = transcoded_request["method"] headers = dict(metadata) headers["Content-Type"] = "application/json" response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), ) return response def __call__( self, request: model_service.GetTunedModelRequest, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> tuned_model.TunedModel: r"""Call the get tuned model method over HTTP. Args: request (~.model_service.GetTunedModelRequest): The request object. Request for getting information about a specific Model. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: ~.tuned_model.TunedModel: A fine-tuned model created using ModelService.CreateTunedModel. """ http_options = ( _BaseModelServiceRestTransport._BaseGetTunedModel._get_http_options() ) request, metadata = self._interceptor.pre_get_tuned_model(request, metadata) transcoded_request = _BaseModelServiceRestTransport._BaseGetTunedModel._get_transcoded_request( http_options, request ) # Jsonify the query params query_params = _BaseModelServiceRestTransport._BaseGetTunedModel._get_query_params_json( transcoded_request ) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER request_url = "{host}{uri}".format( host=self._host, uri=transcoded_request["uri"] ) method = transcoded_request["method"] try: request_payload = type(request).to_json(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.ai.generativelanguage_v1alpha.ModelServiceClient.GetTunedModel", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "GetTunedModel", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = ModelServiceRestTransport._GetTunedModel._get_response( self._host, metadata, query_params, self._session, timeout, transcoded_request, ) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = tuned_model.TunedModel() pb_resp = tuned_model.TunedModel.pb(resp) json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_get_tuned_model(resp) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER try: response_payload = tuned_model.TunedModel.to_json(response) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.ai.generativelanguage_v1alpha.ModelServiceClient.get_tuned_model", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "GetTunedModel", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _ListModels( _BaseModelServiceRestTransport._BaseListModels, ModelServiceRestStub ): def __hash__(self): return hash("ModelServiceRestTransport.ListModels") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None, ): uri = transcoded_request["uri"] method = transcoded_request["method"] headers = dict(metadata) headers["Content-Type"] = "application/json" response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), ) return response def __call__( self, request: model_service.ListModelsRequest, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> model_service.ListModelsResponse: r"""Call the list models method over HTTP. Args: request (~.model_service.ListModelsRequest): The request object. Request for listing all Models. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: ~.model_service.ListModelsResponse: Response from ``ListModel`` containing a paginated list of Models. """ http_options = ( _BaseModelServiceRestTransport._BaseListModels._get_http_options() ) request, metadata = self._interceptor.pre_list_models(request, metadata) transcoded_request = ( _BaseModelServiceRestTransport._BaseListModels._get_transcoded_request( http_options, request ) ) # Jsonify the query params query_params = ( _BaseModelServiceRestTransport._BaseListModels._get_query_params_json( transcoded_request ) ) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER request_url = "{host}{uri}".format( host=self._host, uri=transcoded_request["uri"] ) method = transcoded_request["method"] try: request_payload = type(request).to_json(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.ai.generativelanguage_v1alpha.ModelServiceClient.ListModels", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "ListModels", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = ModelServiceRestTransport._ListModels._get_response( self._host, metadata, query_params, self._session, timeout, transcoded_request, ) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = model_service.ListModelsResponse() pb_resp = model_service.ListModelsResponse.pb(resp) json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_list_models(resp) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER try: response_payload = model_service.ListModelsResponse.to_json( response ) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.ai.generativelanguage_v1alpha.ModelServiceClient.list_models", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "ListModels", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _ListTunedModels( _BaseModelServiceRestTransport._BaseListTunedModels, ModelServiceRestStub ): def __hash__(self): return hash("ModelServiceRestTransport.ListTunedModels") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None, ): uri = transcoded_request["uri"] method = transcoded_request["method"] headers = dict(metadata) headers["Content-Type"] = "application/json" response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), ) return response def __call__( self, request: model_service.ListTunedModelsRequest, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> model_service.ListTunedModelsResponse: r"""Call the list tuned models method over HTTP. Args: request (~.model_service.ListTunedModelsRequest): The request object. Request for listing TunedModels. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: ~.model_service.ListTunedModelsResponse: Response from ``ListTunedModels`` containing a paginated list of Models. """ http_options = ( _BaseModelServiceRestTransport._BaseListTunedModels._get_http_options() ) request, metadata = self._interceptor.pre_list_tuned_models( request, metadata ) transcoded_request = _BaseModelServiceRestTransport._BaseListTunedModels._get_transcoded_request( http_options, request ) # Jsonify the query params query_params = _BaseModelServiceRestTransport._BaseListTunedModels._get_query_params_json( transcoded_request ) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER request_url = "{host}{uri}".format( host=self._host, uri=transcoded_request["uri"] ) method = transcoded_request["method"] try: request_payload = type(request).to_json(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.ai.generativelanguage_v1alpha.ModelServiceClient.ListTunedModels", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "ListTunedModels", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = ModelServiceRestTransport._ListTunedModels._get_response( self._host, metadata, query_params, self._session, timeout, transcoded_request, ) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = model_service.ListTunedModelsResponse() pb_resp = model_service.ListTunedModelsResponse.pb(resp) json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_list_tuned_models(resp) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER try: response_payload = model_service.ListTunedModelsResponse.to_json( response ) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.ai.generativelanguage_v1alpha.ModelServiceClient.list_tuned_models", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "ListTunedModels", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _UpdateTunedModel( _BaseModelServiceRestTransport._BaseUpdateTunedModel, ModelServiceRestStub ): def __hash__(self): return hash("ModelServiceRestTransport.UpdateTunedModel") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None, ): uri = transcoded_request["uri"] method = transcoded_request["method"] headers = dict(metadata) headers["Content-Type"] = "application/json" response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, ) return response def __call__( self, request: model_service.UpdateTunedModelRequest, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> gag_tuned_model.TunedModel: r"""Call the update tuned model method over HTTP. Args: request (~.model_service.UpdateTunedModelRequest): The request object. Request to update a TunedModel. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: ~.gag_tuned_model.TunedModel: A fine-tuned model created using ModelService.CreateTunedModel. """ http_options = ( _BaseModelServiceRestTransport._BaseUpdateTunedModel._get_http_options() ) request, metadata = self._interceptor.pre_update_tuned_model( request, metadata ) transcoded_request = _BaseModelServiceRestTransport._BaseUpdateTunedModel._get_transcoded_request( http_options, request ) body = _BaseModelServiceRestTransport._BaseUpdateTunedModel._get_request_body_json( transcoded_request ) # Jsonify the query params query_params = _BaseModelServiceRestTransport._BaseUpdateTunedModel._get_query_params_json( transcoded_request ) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER request_url = "{host}{uri}".format( host=self._host, uri=transcoded_request["uri"] ) method = transcoded_request["method"] try: request_payload = type(request).to_json(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.ai.generativelanguage_v1alpha.ModelServiceClient.UpdateTunedModel", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "UpdateTunedModel", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = ModelServiceRestTransport._UpdateTunedModel._get_response( self._host, metadata, query_params, self._session, timeout, transcoded_request, body, ) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = gag_tuned_model.TunedModel() pb_resp = gag_tuned_model.TunedModel.pb(resp) json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_update_tuned_model(resp) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER try: response_payload = gag_tuned_model.TunedModel.to_json(response) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.ai.generativelanguage_v1alpha.ModelServiceClient.update_tuned_model", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "UpdateTunedModel", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp @property def create_tuned_model( self, ) -> Callable[[model_service.CreateTunedModelRequest], operations_pb2.Operation]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._CreateTunedModel(self._session, self._host, self._interceptor) # type: ignore @property def delete_tuned_model( self, ) -> Callable[[model_service.DeleteTunedModelRequest], empty_pb2.Empty]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._DeleteTunedModel(self._session, self._host, self._interceptor) # type: ignore @property def get_model(self) -> Callable[[model_service.GetModelRequest], model.Model]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._GetModel(self._session, self._host, self._interceptor) # type: ignore @property def get_tuned_model( self, ) -> Callable[[model_service.GetTunedModelRequest], tuned_model.TunedModel]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._GetTunedModel(self._session, self._host, self._interceptor) # type: ignore @property def list_models( self, ) -> Callable[[model_service.ListModelsRequest], model_service.ListModelsResponse]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._ListModels(self._session, self._host, self._interceptor) # type: ignore @property def list_tuned_models( self, ) -> Callable[ [model_service.ListTunedModelsRequest], model_service.ListTunedModelsResponse ]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._ListTunedModels(self._session, self._host, self._interceptor) # type: ignore @property def update_tuned_model( self, ) -> Callable[[model_service.UpdateTunedModelRequest], gag_tuned_model.TunedModel]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._UpdateTunedModel(self._session, self._host, self._interceptor) # type: ignore @property def get_operation(self): return self._GetOperation(self._session, self._host, self._interceptor) # type: ignore class _GetOperation( _BaseModelServiceRestTransport._BaseGetOperation, ModelServiceRestStub ): def __hash__(self): return hash("ModelServiceRestTransport.GetOperation") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None, ): uri = transcoded_request["uri"] method = transcoded_request["method"] headers = dict(metadata) headers["Content-Type"] = "application/json" response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), ) return response def __call__( self, request: operations_pb2.GetOperationRequest, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> operations_pb2.Operation: r"""Call the get operation method over HTTP. Args: request (operations_pb2.GetOperationRequest): The request object for GetOperation method. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: operations_pb2.Operation: Response from GetOperation method. """ http_options = ( _BaseModelServiceRestTransport._BaseGetOperation._get_http_options() ) request, metadata = self._interceptor.pre_get_operation(request, metadata) transcoded_request = _BaseModelServiceRestTransport._BaseGetOperation._get_transcoded_request( http_options, request ) # Jsonify the query params query_params = ( _BaseModelServiceRestTransport._BaseGetOperation._get_query_params_json( transcoded_request ) ) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER request_url = "{host}{uri}".format( host=self._host, uri=transcoded_request["uri"] ) method = transcoded_request["method"] try: request_payload = json_format.MessageToJson(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.ai.generativelanguage_v1alpha.ModelServiceClient.GetOperation", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "GetOperation", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = ModelServiceRestTransport._GetOperation._get_response( self._host, metadata, query_params, self._session, timeout, transcoded_request, ) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) content = response.content.decode("utf-8") resp = operations_pb2.Operation() resp = json_format.Parse(content, resp) resp = self._interceptor.post_get_operation(resp) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER try: response_payload = json_format.MessageToJson(resp) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.ai.generativelanguage_v1alpha.ModelServiceAsyncClient.GetOperation", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "GetOperation", "httpResponse": http_response, "metadata": http_response["headers"], }, ) return resp @property def list_operations(self): return self._ListOperations(self._session, self._host, self._interceptor) # type: ignore class _ListOperations( _BaseModelServiceRestTransport._BaseListOperations, ModelServiceRestStub ): def __hash__(self): return hash("ModelServiceRestTransport.ListOperations") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None, ): uri = transcoded_request["uri"] method = transcoded_request["method"] headers = dict(metadata) headers["Content-Type"] = "application/json" response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), ) return response def __call__( self, request: operations_pb2.ListOperationsRequest, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> operations_pb2.ListOperationsResponse: r"""Call the list operations method over HTTP. Args: request (operations_pb2.ListOperationsRequest): The request object for ListOperations method. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: operations_pb2.ListOperationsResponse: Response from ListOperations method. """ http_options = ( _BaseModelServiceRestTransport._BaseListOperations._get_http_options() ) request, metadata = self._interceptor.pre_list_operations(request, metadata) transcoded_request = _BaseModelServiceRestTransport._BaseListOperations._get_transcoded_request( http_options, request ) # Jsonify the query params query_params = _BaseModelServiceRestTransport._BaseListOperations._get_query_params_json( transcoded_request ) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER request_url = "{host}{uri}".format( host=self._host, uri=transcoded_request["uri"] ) method = transcoded_request["method"] try: request_payload = json_format.MessageToJson(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.ai.generativelanguage_v1alpha.ModelServiceClient.ListOperations", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "ListOperations", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = ModelServiceRestTransport._ListOperations._get_response( self._host, metadata, query_params, self._session, timeout, transcoded_request, ) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) content = response.content.decode("utf-8") resp = operations_pb2.ListOperationsResponse() resp = json_format.Parse(content, resp) resp = self._interceptor.post_list_operations(resp) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( logging.DEBUG ): # pragma: NO COVER try: response_payload = json_format.MessageToJson(resp) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.ai.generativelanguage_v1alpha.ModelServiceAsyncClient.ListOperations", extra={ "serviceName": "google.ai.generativelanguage.v1alpha.ModelService", "rpcName": "ListOperations", "httpResponse": http_response, "metadata": http_response["headers"], }, ) return resp @property def kind(self) -> str: return "rest" def close(self): self._session.close() __all__ = ("ModelServiceRestTransport",)